This document contains a weather forecast and analysis of temperature and other weather data from Szeged, Hungary between 2006-2016. It includes histograms, boxplots and correlation analyses of temperature vs other factors like apparent temperature, precipitation type, wind speed, humidity and more. There are also linear regression models to predict temperature. While temperature can be predicted fairly accurately during snow, prediction is less accurate during rain due to higher variability in temperatures.

1. TEMPERATURE
A WEATHER FORECAST BY TEAM 9
PREDICTION
LIVE

2. ??
Introduction
The dataset weather.csv was studied by
Norbert Budincsevity
Weather in Szeged, Hungary
from 2006 to 2016

3. Formatted Date
(DD/MM/YY, MM/DD/YY, YY/MM/DD,
Month D, Yr)
A weather forecast
Temperature
Wind Bearing
Wind Speed
Daily
weather summary

4. A weather forecast

5. Apparent temperature: the temperature perceived by humans
(sensation)
Visibility: the degree to which distant objects can be seen, is
affected by weather conditions.
Cloud Cover: the fraction of the sky obscured by clouds, as
observed from a particular location.
Pressure: Atmospheric pressure affect weather, with low-
pressure -> cloudiness, wind, and precipitation. High-pressure
systems -> fair, calm weather.
Apparent temperature: the temperature perceived by humans
(sensation)
Visibility: the degree to which distant objects can be seen, is
affected by weather conditions.
Cloud Cover: the fraction of the sky obscured by clouds, as
observed from a particular location.
Pressure: Atmospheric pressure affect weather, with low-
pressure -> cloudiness, wind, and precipitation. High-pressure
systems -> fair, calm weather.
A weather forecast

6. All the data we computed

7. Symmetric
BASIC INSIGHTS FROM THE DATA
HISTOGRAM OF TEMPERATURE

8. BOXPLOT
x = -21.822222 degrees
min
x = 39.905556 degrees
max
Mean = 11.932678 degrees
Outliers on the minimum side
Q3= 18.838889
Q2 = median= 12
Q1= 4.688889

9. When it is snowing, the
temperature ranges between
-20 degrees to 0 degrees
When it is raining, the
temperature ranges between
0 degrees to 40 degrees.
r = 0.9926
Strong, positive relationship
The correlation between
Temperature and Apparent Temp

10. The correlation between
Temperature and PrecipType
Symmetric box plot
Rain box plot
Right-skewed distribution
Snow box plot
Max= 39.905555560
Q3 = 19.894444440
Q2 (median) = 13.272222220
Q1 = 7.172222222
Min = 0.005555556
Max= 0
Q3 = -1.094444
Q2 (median) = -2.311111
Q1 =-4.861111
Min = -10

11. Median: Raining is higher than snowing
(temperature).
IQR: Rain box plot is more dispersed than Snow
box plot (temperature).
Comparing
Outliers:
- Snow box plot has a wider distribution below the
minimum (more scattered data)
- Rain box plot has a narrower distribution above
the maximum.
Snow falls
-> Much colder and temps.
fall far beyond minimum
Rain falls:
-> Getting hotter but are
not so common

12. -> Nearly no clear correlation
-> ↑temperature →a little growth
in wind speed
Temperature
(°C)
Windspeed
(km/h)
Dots
In every °C 0-23 km/h Most common
In every °C 23~40 km/h
Smaller limit
of dots
0-20°C 40 km/h ↑ Merely
existence
9°C 63 km/h Highest
Correlation coefficient: 0.009

13. Temperature (°C) Windbearing
40°C 250
-22°C 320
<-17°C 0-90; 155-230; 320-350
>35°C All
Correlation coefficient: 0.03
-> Nearly no clear correlation between the two variables.
> 120: more numbers of temperature
indexes are recorded.

14. Temperature(°
C)
Humidity
(g/m3)
Dots
0°C
0.3125g/m3 -
0.9g/m3
Both kinds
are densely
packed
-10°C
0.0625g/m3 -
0.375g/m3
No snow
↑0°C 0.125g/m3 No rain
Correlation coefficient = -0.6323

15. Weak positive correlation: 0.3928
10.5 km: most common, occurs at every
temperature
0->10 km: density spreads evenly.
10->15 km: upward fluctuation
12.5->13.75 km: hardly occurs
-> The higher the visibility the more likely
for the temperature to be high

16. Subtle negative correlation: -0.0054
25% (1011.9)-> 75% (1021):
tightly packed
-> Almost no linear relationship

17. REGRESSION MODEL
<0.05
<0.05
HIGHEST
R-SQUARED
LOWEST
RS ERROR

18. PREDICTION VS ACTUAL

19. Explained 60% of the variance of temperature but 40% left unexplained.
The model is fairly efficient to predict temperature during snowing days
but the temperature during raining days tend to have larger variance.
CONCLUSION

20. THANK YOU!
THANK YOU!